Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

Authors

Leonardo Laipelt, Federal University of Rio Grande do SulFollow
Anderson Luis Ruhoff, Federal University of Rio Grande do Sul and University of Nebraska-Lincoln
Ayan Santos Fleischmann, Federal University of Rio Grande do SulFollow
Rafael Henrique Bloedow Kayser, Federal University of Rio Grande do SulFollow
Elisa de Mello Kich, Federal University of Rio Grande do SulFollow
Humberto Ribeiro da Rocha, University of Sao PauloFollow
Christopher Michael Usher Neale, University of Nebraska-LincolnFollow

ORCID IDs

Anderson Luis Ruhoff

Date of this Version

2020

Document Type

Article

Citation

Remote Sens. 2020, 12, 1108; doi:10.3390/rs12071108

Comments

© 2020 by the authors.

Abstract

Evapotranspiration (ET) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.